Colorization is the practice of adding appropriate chromatic values to monochrome photographs or videos. A real-valued luminance image can be mapped to a three-dimensional color image. However, it is a severely ill-defined problem and not has a single solution. In this paper, an encoder-decoder Convolutional Neural Network (CNN) model is used for colorizing gray images where the encoder is a Densely Connected Convolutional Network (DenseNet) and the decoder is a conventional CNN. The DenseNet extracts image features from gray images and the conventional CNN outputs a * b * color channels. Due to a large number of desaturated color components compared to saturated color components in the training images, the saturated color components have a strong tendency towards desaturated color components in the predicted a * b * channel. To solve the problems, we rebalance the predicted a * b * color channel by smoothing every subregion individually using the average filter. 2 stage k-means clustering technique is applied to divide the subregions. Then we apply Gamma transformation in the entire a * b * channel to saturate the image. We compare our proposed method with several existing methods. From the experimental results, we see that our proposed method has made some notable improvements over the existing methods and color representation of gray-scale images by our proposed method is more plausible to visualize. Additionally, our suggested approach beats other approaches in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Histogram.

Gray to Color conversion causes difficulties because of the nature of its intrinsic multimodality. Despite recent significant advancements in this domain by numerous learning-based approaches, there still have two drawbacks: i) implausible color assignment and ii) contextual ambiguity. Recently deep learning models are being used for colorization as they outperform others. In a training image, desaturated color components are greater than saturated color components due to the larger background areas (clouds, pavement, dirt, walls, etc.) compared to the focused objects. This imbalanced feature representation biases the learning model in favor of major features. However, small regions with specific colors are the region of interest. To solve this problem, we proposed the Deep Localization Network (DL-Net) by modifying the mean squared error backpropagation algorithm. We compute chromatic component-based Local Losses (LLs) which are the primary component of the proposed DL-Net. The LL employs priority on rare semantic components of the original image features. It works to improve diverse-range dependency modeling in an effort to reduce contextual ambiguity and color leakage that promotes the production of more plausible coloring. With a number of current methodologies, we contrast our proposed approach. The experimental findings demonstrate that our proposed method produces good colorization of images and outperforms other methods in terms of SSIM, MSE, and PSNR quality criteria.

The rapid advancements in computer technology and the internet’s acceptance in every aspect of our lives, particularly in recent years, have made students and instructors vital in the teaching and learning sector. Web-based studies have also brought about advances in the education area, and numerous applications have become widespread in this field. In this paper, we suggested an online test multiple-choice question assessment system for students called the Online Exam System (OES). This system may be used by any university, college, or institution that has a computerized education system. The OES can be used by teachers to administer quizzes. The system will calculate the participant’s performance based on his response, and the following question will be created based on the participant’s performance.   After the examination, the system will display the results and offer feedback based on the participant’s request. Administrative control over the entire system is available. A teacher has authority over the question bank and is responsible for creating test schedules. Therefore, the project will be very helpful for the beginner and mid-level programming learners. And also, will give a proper guideline to the students who are willing to learn programming and introduce the users with competitive programming and problem-solving skills.