A Robust Development of High Dynamic Range Image Contrast Enhancement Methodology using Learning Evaluations

The construction of an image that is extremely realistic may be accomplished through the procedure for going from a low-light to a high-dynamic-range picture. This procedure does not require the use of expensive equipment. Recent developments in deep learning have made it feasible to create high dynamic range (HDR) photographs that are both intelligent and realistic for the first time. This research presents a deep learning strategy for differentiating the bright and dark parts of an input “Low Dynamic Range (LDR)” image. The goal of this technique is to reconstruct an HDR image with dynamic ranges that are similar to those seen in the real world. A “High-Dynamic-Range (HDR)” image is produced by the multi-stage deep learning network that has been developed. This network combines characteristics that have a larger range of brightness, which allows it to increase areas of high light and diminish areas of low light. Creating an HDR image that seems and feels realistic may be accomplished by first splitting the LDR image into bright and dark sections. This allows for the information about under- and over-exposed areas to be efficiently utilized in the creation of the HDR image. Our goal in developing the Elevated Learning based Dynamic Image Filter (ELDIF) was to improve upon previous models' performance in high dynamic range (HDR) imaging. To test how well this new model works, we cross-validated it with the more traditional High Dynamic Range Image Filter (HDRIF). The appropriate evidence for the suggested model's efficiency is provided in the subsequent section.