Hardware Acceleration for Adaptive Gamma Correction in Embedded Systems
İlayda Sarıçam Master Student
(Supervisor: Prof.Dr.Uğur Güdükbay) Computer Engineering Department
Bilkent University
Abstract: Enhancing images in low-light conditions is a critical task in various domains, including photography, security systems, military, and autonomous driving models. These fields often require image processing and analysis tasks in low-light images due to a lack of lighting sources and shadows. However, there are limitations and bottlenecks in low-light image enhancement, such as under-enhancement, over-enhancement, and high power consumption.
This thesis introduces a region-wise adaptive gamma correction (AGC) method, which is a non-learning-based approach, to enhance the visibility of low-light images. In this study, to select the optimal gamma value adaptively, the image is partitioned into regions based on detected edges and ridges. Then, the optimal gamma value for each region is computed from average intensity, brightness, luminance, and RGB values. As a result, the gamma correction is applied to each region separately. With this region-wise approach, under-enhancement and over-enhancement of the input image are prevented. Furthermore, our approach is tailored for low-light image enhancement tasks in power-limited systems. Therefore, our implementation uses low-power devices rather than high-performance GPUs and CPUs, as typically used in the literature. To evaluate our results and output images, we use Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Mean Squared Error (MSE), Naturalness Image Quality Assessor (NIQE), runtime, and power consumption as evaluation metrics. Also, to observe the effectiveness of our approach and compare it with prior studies, we conducted experiments on two datasets, namely, LOL and MIT-Adobe FiveK. When compared with previous non-learning-based methods, our approach achieves a twofold improvement in PSNR. Furthermore, we reduce the power consumption of the low-light image enhancement by more than 250X.
DATE: January 12, Monday @ 11:00 Place: EA 516