BanglaLekha-Isolated, a Bangla handwritten isolated character dataset is presented in this article. This dataset contains 84 different characters comprising of 50 Bangla basic characters, 10 Bangla numerals and 24 selected compound characters. 2000 handwriting samples for each of the 84 characters were collected, digitized and pre-processed. After discarding mistakes and scribbles, 1,66,105 handwritten character images were included in the final dataset. The dataset also includes labels indicating the age and the gender of the subjects from whom the samples were collected. This dataset could be used not only for optical handwriting recognition research but also to explore the influence of gender and age on handwriting. The dataset is publicly available at https://data.mendeley.com/datasets/hf6sf8zrkc/2.

Image denoising performs a prominent role in medical image analysis. In many cases, it can drastically accelerate the diagnostic process by enhancing the perceptual quality of noisy image samples. However, despite the extensive practicability of medical image denoising, the existing denoising methods illustrate deficiencies in addressing the diverse range of noise appears in the multidisciplinary medical images. This study alleviates such challenging denoising task by learning residual noise from a substantial extent of data samples. Additionally, the proposed method accelerates the learning process by introducing a novel deep network, where the network architecture exploits the feature correlation known as the attention mechanism and combines it with spatially refine residual features. The experimental results illustrate that the proposed method can outperform the existing works by a substantial margin in both quantitative and qualitative comparisons. Also, the proposed method can handle real-world image noise and can improve the performance of different medical image analysis tasks without producing any visually disturbing artefacts.

Bangla is one of the most widely used languages worldwide. This paper presents an application of image retrieval techniques to automatically judge the aesthetic quality of handwritten Bangla isolated characters. Retrieval techniques are also adapted to give improvement suggestions, with a plan to incorporate the methods in applications which can assist in learning/teaching handwriting. The proposed method borrows key concepts from content-based image retrieval. Our method was tested on the BanglaLekha-Isolated data set, which contains images of 84 Bangla characters, with nearly 2000 samples per character. The data set contains evaluation of the aesthetic quality of the handwriting judged on a scale of 1-5. For this work, the dataset was partitioned into a test set of 400 images and a database-set of ≈ 1600 images, per Bangla character. Assuming that a scoring difference of 1 is acceptable, the proposed method achieves an accuracy of 77.39% when using features extracted by a convolutional neural network based autoencoder. Experiments were also done with the popular HOG feature. However, the autoencoder-based results showed clear superiority compared the HOG-based results. Our proposed method for improvement suggestions also shows that it is possible to shows samples from the dataset which will help users improve their handwriting while requiring small changes to their own handwriting.

Single-shot image deblurring in a low-light condition is known to be a profoundly challenging image translation task. This study tackles the limitations of the low-light image deblurring with a learning-based approach and proposes a novel deep network named as DarkDeblurNet. The proposed DarkDeblur- Net comprises a dense-attention block and a contextual gating mechanism in a feature pyramid structure to leverage content awareness. The model additionally incorporates a multi-term objective function to perceive a plausible perceptual image quality while performing image deblurring in the low-light settings. The practicability of the proposed model has been verified by fusing it in numerous computer vision applications. Apart from that, this study introduces a benchmark dataset collected with actual hardware to assess the low-light image deblurring methods in a real-world setup. The experimental results illustrate that the proposed method can outperform the state-of-the-art methods in both synthesized and real-world data for single-shot image deblurring, even in challenging lighting environment.

Due to numerous hardware shortcomings, medical image acquisition devices are susceptible to producing low-quality (i.e., low contrast, inappropriate brightness, noisy, etc.) images. Regrettably, perceptually degraded images directly impact the diagnosis process and make the decision-making manoeuvre of medical practitioners notably complicated. This study proposes to enhance such low-quality images by incorporating end-to-end learning strategies for accelerating medical image analysis tasks. To the best concern, this is the first work in medical imaging which comprehensively tackles perceptual enhancement, including contrast correction, luminance correction, denoising, etc., with a fully convolutional deep network. The proposed network leverages residual blocks and a residual gating mechanism for diminishing visual artefacts and is guided by a multi-term objective function to perceive the perceptually plausible enhanced images. The practicability of the deep medical image enhancement method has been extensively investigated with sophisticated experiments. The experimental outcomes illustrate that the proposed method could outperform the existing enhancement methods for different medical image modalities by 5.00 to 7.00 dB in peak signal-to-noise ratio (PSNR) metrics and 4.00 to 6.00 in DeltaE metrics. Additionally, the proposed method can drastically improve the medical image analysis tasks' performance and reveal the potentiality of such an enhancement method in real-world applications. Code Available: https://github.com/sharif-apu/DPE_JBHI