Everything you need to know about image processing app development

Intro

Businesses are turning to image processing applications to solve challenges in the real world. The versatility of these apps makes them valuable across various sectors. As an example, a retail business might utilize an image processing app to track inventory through shelf images. Retail companies that employ image recognition in their processes report a 35% increase in the efficiency of their processes. At the same time, 65% of customers say they prefer retailers who use AI-powered technologies. 

This article explores the key features and general business impact of image processing apps. Our team also offers insights into creating Voila, one of the most popular iOS and Android applications for image processing, and Netapix, a photo editing app, to illustrate the development process.

Overview of image processing apps

So, what are image processing apps in their essence? These are solutions designed to examine, adjust, and understand visual data. They use data-driven models and intelligent algorithms to perform tasks like face and object detection, image enhancement, and pattern recognition. These apps can integrate with existing systems to provide insights from visual materials. 

Why image processing apps are gaining popularity

To better comprehend the landscape of image processing apps, we at Touchlane analyzed their market size and adoption statistics. Here are some useful insights. 

• In 2024, AI image editors accounted for an estimated $80.3 million in market value.
• AI image editors and generators saw the fastest growth on G2 in 2024, with a 441% year-over-year increase. AI chatbots ranked second at 252%.
• More than 58% of respondents in a recent Pincel survey said they tried AI image editors. 

The rise of image processing apps is closely tied to the growing availability of advanced technologies like artificial intelligence and cloud computing. Businesses are recognizing the potential of these tools to address challenges that were once time-consuming or expensive to solve. 

As an illustration, image processing apps are widely used in fintech for document verification and fraud detection. Companies such as Jumio and Onfido employ image recognition technology to authenticate identities by examining photos of IDs, passports, and similar documents. 

Key features of image processing apps

Whether handling simple adjustments or advanced AI-driven enhancements, the right features can make a significant difference in usability and performance. Here are the essential capabilities to consider.

Image editing tools

A solid image processing app includes basic editing options such as color adjustments, cropping, resizing, and filters. With these features, users can modify images quickly without relying on external software.

Advanced features

More sophisticated applications incorporate AI and machine learning to offer object detection, automatic background removal, and smart enhancements. These capabilities help users improve image quality with minimal effort.

Image processing in real time

Certain applications handle images immediately so users can see changes as they apply edits. This feature is highly effective for operations such as augmented reality applications, live photo effects, and instant previews.

Cloud integration for storage and backup

Many users prefer storing and accessing their images across multiple devices. Cloud support allows them to save processed images securely and avoid the limitations of device storage and processing power. This approach makes it possible to handle complex tasks without overloading local resources.

Social media sharing and integration

Direct sharing options make it easy to publish images across various platforms. Some applications also include built-in integrations with tools for content creation to speed up workflow.

User-friendly interface and customization options

A clear layout with straightforward helps users complete tasks without unnecessary steps. Some apps offer customization settings that allow users to adjust workflows to match their preferences.

Technology stack for image processing apps

Here is a breakdown of the key components that make up a robust technology stack for image processing applications, illustrated with our insights from the development of Voila, a popular AI-powered face art editor, and Netapix, a photo enhancing application.

! For Netapix and Voila, we started the development process in 2018 and 2020, respectively. 

Frontend technologies

The frontend is the face of your app, directly interacting with users. For image processing apps, the frontend must handle tasks like photo uploads, real-time previews, and user interactions smoothly. Native development for iOS or Android is often preferred for these demanding functionalities. 

In the case of Voila, the team used Swift and Kotlin to produce a native experience for Apple and Android users. The languages’ performance and compatibility with native ecosystems made them ideal for integrating features like camera access, photo library integration, and face detection. 

Netapix specifically relied on CoreML to process high-resolution images directly on user devices and allow complex transformations without requiring cloud-based computing.

Backend technologies

The backend handles the heavy lifting, such as data storage, image processing, and user management. Node.js is a popular choice for backend development due to its scalability and efficiency in handling real-time operations. It pairs well with image processing tasks, especially when combined with libraries like TensorFlow or OpenCV.

For Voila, the backend was responsible for applying AI-driven effects like cartoonization and Renaissance-style transformations. The team used Firebase for backend services, including user authentication, data storage, and real-time updates. Firebase’s integration with other tools, such as Alamofire for network requests, supported smooth communication between the frontend and backend. 

Netapix, in contrast, focused on on-device processing to reduce the reliance on backend services for image manipulation. The project involved developing a neural network model trained with a large dataset of photos and preset styles created by photographers. Our team used Python, Keras, and convolutional neural network-based architectures to train the model and produce efficient execution within the memory constraints of mobile devices.

Image processing libraries and tools

The core of any image processing application lies in its ability to manipulate images effectively. Libraries like OpenCV and TensorFlow are industry standards for tasks such as face detection, object recognition, and style transfer. These tools provide pre-built algorithms that significantly reduce development time while maintaining high accuracy.

In Voila, the team used the Vision framework for face detection on the client side and precise identification of facial features. The backend employed custom AI algorithms to transform photos into artistic renditions. 

With Netapix, we took a different approach by implementing CoreML and CIImage for real-time image transformation on iOS devices. The app applied over 100 pre-trained neural network models as filters, which allowed users to achieve professional-level photo edits without internet connectivity.

Cloud platforms

Cloud platforms like AWS, Google Cloud, and Microsoft Azure provide the infrastructure needed to support image processing apps. They offer scalable storage, powerful computing resources, and pre-built AI services that can be integrated into your app. These platforms also handle tasks like data backup, security, and load balancing.

For Voila, cloud services were instrumental in managing user data, processing images, and deploying updates. The app’s ability to handle 30,000 daily active users without performance issues highlights the importance of a reliable cloud infrastructure. 

In contrast, Netapix handles image transformations directly on user devices instead of relying on cloud-based processing. Using this approach, our team eliminated infrastructure costs while providing instant results. Nevertheless, Netapix required careful neural network optimization to fit within device constraints.

What it means for your business

The technology stack for an image processing app must balance performance, scalability, and user experience. The Netapix project demonstrated how modern chips combined with frameworks such as Metal and CoreML expand the possibilities of running machine learning models on mobile devices. Voila’s success, supported by its 4.8 rating on the App Store, highlighted how the right combination of frontend and backend technologies, powerful image processing libraries, and solid cloud platforms can create a product that resonates with users.

Factors affecting image processing app development cost

Having considered the information above, you may ask yourself a question – how much will your image processing app cost? Below, we share the major factors affecting the final price.

• Functionality. Basic functions of image processing apps – like filters and cropping – require much less effort and expenses than advanced ones, such as AI-driven object recognition or real-time enhancements. 
• Choice of platform. The development of an iOS- or Android-only image processing app can save money. 
• Design and UI requirements. The more polished and engaging the experience, the higher the investment. 
• Third-party services and APIs. Connecting the app to cloud storage, AI-powered image processing, or social media platforms requires extra development time. 

Expertise and location of your development team. Rates vary based on geography, with teams in North America and Western Europe charging more than those in Eastern Europe, Latin America, or Asia. A highly experienced team may charge higher rates but deliver better results in less time.

Maintenance and updates for image processing apps

Once an image processing app is launched, regular maintenance keeps it reliable and secure. 

Ongoing maintenance requirements

Server monitoring, API compatibility checks, and security patches help prevent downtime and vulnerabilities. As third-party libraries and frameworks change, adjustments are necessary to keep the app functional.

Regular updates and feature enhancements

Expanding format support, refining algorithms, or introducing AI-driven improvements keeps the app competitive. Newer hardware and operating systems may require adjustments to maintain performance.

Handling bug fixes and performance improvements

Swift identification and resolution of bugs prevent disruptions. Performance tuning, such as faster image processing times or lower resource consumption, improves user experience. Regular audits help detect inefficiencies before they affect operations.

How to choose the right development team

Choosing a development team for an image processing app requires a company with proven experience in designing top-performing mobile applications, as well as visual data processing. Expertise in AI-driven recognition, computer vision, and tools like OpenCV or TensorFlow is crucial. Reviewing case studies, speaking with past clients, and discussing technical challenges with developers will reveal their capabilities.

Conclusion

Building an image processing app requires thoughtful planning, the right technology stack, as seen in the cases of Voila and Netapix, and a skilled development team. With the market expanding and businesses finding new ways to use visual data, the potential for innovation in this space continues to grow. A well-executed app can attract a tech-savvy user base and open new revenue opportunities.

If you are considering developing an image processing app, our team at Touchlane can help. Get in touch to discuss your project and explore how to bring your idea to life.