# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 3 Image Pre-Processing and Selection  
The input image data affect subsequent steps of an object detection and recognition solution. In particular, when captured by non-experts or visually impaired people, the pre-processing and selection of the images are important steps. Image pre-processing techniques can be divided into two main groups: corrections, e.g., of lighting, noise or colour; enhancements, e.g., illumination, blur or focus. In [22], the authors analysed the calibration parameters (camera distortion correction and gamma correction) of Micro Aerial Vehicles (MAVs) and Unmanned Aerial Vehicles (UAVs) and image parameters (quantization, compression, resolution, colour model, additional channels). By analysing each parameter individually, they could demonstrate the different impacts each one has on object detection. While some require more memory and others do not perform noticeably differently, camera resolution was discovered to be crucial to this task. Cropping, filtering, rotating and flipping images is frequently performed during pre-processing. Typically, these tasks are completed manually, with a bulk of images undergoing the same transformations. To automatise this process, the researchers of [23] created a deep reinforcement learning framework with an agent in charge of deciding which, and if, an image needs additional transformation, and created an environment that performs these changes. They concluded that this method assisted in automating the pre-processing of different data types, primarily images. To process fast low-light images, [PERSON] et al. [24] proposed an end-to-end learning approach employing a fully convolutional network. The outcomes demonstrated effective noise suppression and accurate colour transformation. Real-time processing was achieved for low-resolution images but not for full high-resolution ones. [PERSON] et al. [25] proposed a Generative Adversarial Network for image super-resolution (SR) called SRGAN. The authors achieved this by constructing a perceptual loss function for photo-realistic single-image super-resolution using a ResNet architecture and GAN concepts. Compared to other approaches, this solution produced good visual and objective metrics results. The burst image technique captures several frames that can be combined to produce a higher-resolution image when used with super-resolution algorithms. The BIPNet [26] framework aims to improve and restore burst images. In contrast to other approaches [27; 28] that assume that the scene is static and create a solution to remove noise and enhance low-resolution images, this algorithm focuses on motion. The previously aligned burst image features are concatenated using pseudo-burst features fusion. In contrast to [29], which uses a registration-based super-resolution method on each frame to fuse into a high-resolution image, it uses an edge-boosting burst alignment module to prevent mismatching features. The BIPNet framework is effective for tasks requiring high resolution, low-light enhancement, and denoising. This method has a low computational complexity, which is advantageous when using a mobile device. An approach to denoise burst images captured from a mobile phone was proposed by [PERSON] et al [30]. A fully convolutional deep neural network was used for each frame after it had been stabilised. An improved image was then produced by combining the data from each one using a parallel recurrent network. Despite their promising results, their deep learning strategy was computationally expensive and required considerable memory and processing power. [PERSON] et al. [31] conducted a fast and low-memory-requirement method. They proposed a high dynamic range and image super-resolution reconstruction using raw image bursts as input. This enhanced photos with low-light conditions, noise, camera shake and moderate object motion. This approach improved low-light, noisy, camera-shake and moderately moving object photos. Another approach is a burst super-resolution transformer (BSRT) [32].