Designing a Collection of Portable Image Enhancement for Smartphones

Modern smartphones now offer computational power comparable to desktop systems, enabling a broad range of mobile applications and making in-device photography more widespread. With over six billion active users worldwide, capturing and sharing high-quality images is central to digital life. However, mobile cameras remain limited by noise, blur, haze, and shadows that degrade image quality. This paper presents a five-stage pipeline consisting of Capture, Reduce, Process, Post-process, and Save, designed for image enhancement on resource-constrained mobile devices. The pipeline has been successfully implemented using techniques such as multi-image super-resolution, dehazing, and shadow removal. While each enhancement method requires tailored adjustments at each stage, the pipeline provides a unified framework for deployment. Experimental results on standard benchmarks show that smartphone-based implementations achieve less than 5 percent degradation in structural similarity compared to desktop baselines. These findings demonstrate the feasibility of deploying sophisticated image enhancement algorithms on everyday smartphones, advancing mobile computational photography.