• BHARATHAN.V
• MADHORUBAGAN.E
• DEETCHANA.V
• SUBASH.B

Digital photograph processing packages frequently use photograph augmentation. Despite advances in imaging technology, low-decision pictures are nevertheless broadly utilized in a whole lot of fields, which includes scientific diagnostics, satellite TV for pc imaging, and surveillance. The loss of critical capabilities in those low-decision images makes them vain for sports related to making decisions. The traits of the goal item are more suitable through photograph augmentation, making it less complicated to recognize. For less difficult improving tasks, the Single Image convolutional neural community (SICNN) turned into used to resolve the issue. CNN is designed to have branches and unmarried photograph. Large-scale characteristic department trains the characteristic mappings from low-decision (LR) photograph patches to excessive-decision (HR) photograph patches first. The inflated photograph patches received from bicubic interpolation are the LR photograph patches. Secondly, the characteristic mappings from the down sampled photograph patches to the bigger photograph patches are skilled the usage of the small scale characteristic department. Evaluations carried out on a huge variety of pictures reveal that the cautioned SICNN outperforms the state-of- the-artwork strategies in phrases of each visible nice and numerical result. Deeper architectures, such as Multi-layer Convolution Neural Networks (MLCNNs) with multi-layered trapezoidal convolution kernels, are suggested when categorized data is limited. While SICNNs often demand a large amount of categorized data points for training, deeper architectures can capture more complexity and depth. In this approach, the original image is inputted into the MLCNN, which produces the noise map as its output, facilitating image enhancement. For every 3 channels with inside the unique pictures, this community has a convolution kernel of various sizes. The convolution kernel length is constant through identifying the imply rectangular deviation of the related channel`s pixel values. Moreover, the technique proposed in this study not only enhances aesthetic contrast effectively but also achieves higher Peak Signal to Noise Ratio (PSNR) and a superior Structural Similarity Index (SSIM) compared to existing image enhancement algorithms.

"ENHANCEMENT OF IMAGE RESOLUTION USING MULTILAYER CONVOLUTIONAL NEURAL NETWORK IN MACHINE LEARNING.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.b744-b754, July-2024, Available :http://www.jetir.org/papers/JETIR2407179.pdf

"ENHANCEMENT OF IMAGE RESOLUTION USING MULTILAYER CONVOLUTIONAL NEURAL NETWORK IN MACHINE LEARNING.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppb744-b754, July-2024, Available at : http://www.jetir.org/papers/JETIR2407179.pdf